| Revista: | Computación y sistemas |
| Base de datos: | |
| Número de sistema: | 000560714 |
| ISSN: | 1405-5546 |
| Autores: | Ballinas, Enrique1 Montiel, Óscar1 |
| Instituciones: | 1Instituto Politécnico Nacional, Centro de Investigación y Desarrollo de Tecnología Digital, Ciudad de México. México |
| Año: | 2022 |
| Periodo: | Abr-Jun |
| Volumen: | 26 |
| Número: | 2 |
| Paginación: | 725-742 |
| País: | México |
| Idioma: | Inglés |
| Tipo de documento: | Artículo |
| Resumen en inglés | In this work, a novel Hybrid Quantum Genetic Algorithm (HQGA) for the 0-1 Knapsack Problem (KP) is presented. It is based on quantum computing principles, such as qubits, superposition, and entanglement of states. The HQGA was simulated in the Qiskit simulator. Qiskit simulator is a platform developed by IBM that allows working with quantum computers at the level of circuits, pulses, and algorithms. The performance of HQGA is evaluated in three strongly correlated KP data sets, and computational results are compared with a Quantum-Inspired Evolutionary Algorithm (QIEA), a modified version of a QIEA (QIEA-Q), and a modified version of the HQGA (HQGA-Q). Experimental results demonstrate that the proposed HQGA can obtain the best solutions in all the KP data sets, and performs well on robustness. |
| Disciplinas: | Ciencias de la computación |
| Palabras clave: | Inteligencia artificial |
| Keyword: | Artificial intelligence |
| Texto completo: | Texto completo (Ver HTML) Texto completo (Ver PDF) |
